@article {904, title = {Cigarette smoking and nocturnal sleep architecture.}, journal = {Am J Epidemiol}, volume = {164}, year = {2006}, month = {2006 Sep 15}, pages = {529-37}, abstract = {

Cigarette smoking has been associated with a high prevalence of sleep-related complaints. However, its effects on sleep architecture have not been fully examined. The primary objective of this investigation was to assess the impact of cigarette smoking on sleep architecture. Polysomnography was used to characterize sleep architecture among 6,400 participants of the Sleep Heart Health Study (United States, 1994-1999). Sleep parameters included total sleep time, latency to sleep onset, sleep efficiency, and percentage of time in each sleep stage. The study sample consisted of 2,916 never smokers, 2,705 former smokers, and 779 current smokers. Compared with never smokers, current smokers had a longer initial sleep latency (5.4 minutes, 95\% confidence interval (CI): 2.9, 7.9) and less total sleep time (14.0 minutes, 95\% CI: 6.4, 21.7). Furthermore, relative to never smokers, current smokers also had more stage 1 sleep (relative proportion = 1.24, 95\% CI: 1.14, 1.33) and less slow wave sleep (relative proportion = 0.86, 95\% CI: 0.78, 0.95). Finally, no differences in sleep architecture were noted between former and never smokers. The results of this study show that cigarette smoking is independently associated with disturbances in sleep architecture, including a longer latency to sleep onset and a shift toward lighter stages of sleep. Nicotine in cigarette smoke and acute withdrawal from it may contribute to disturbances in sleep architecture.

}, keywords = {Aged, Arousal, Chi-Square Distribution, Female, Humans, Male, Middle Aged, Polysomnography, Prevalence, Regression Analysis, Risk Factors, Sleep Stages, Sleep Wake Disorders, Smoking, United States}, issn = {0002-9262}, doi = {10.1093/aje/kwj231}, author = {Zhang, Lin and Samet, Jonathan and Caffo, Brian and Punjabi, Naresh M} } @article {1253, title = {Utility of sleep stage transitions in assessing sleep continuity.}, journal = {Sleep}, volume = {33}, year = {2010}, month = {2010 Dec}, pages = {1681-6}, abstract = {

STUDY OBJECTIVES: Sleep continuity is commonly assessed with polysomnographic measures such as sleep efficiency, sleep stage percentages, and the arousal index. The aim of this study was to examine whether the transition rate between different sleep stages could be used as an index of sleep continuity to predict self-reported sleep quality independent of other commonly used metrics.

DESIGN AND SETTING: Analysis of the Sleep Heart Health Study polysomnographic data.

PARTICIPANTS: A community cohort.

MEASUREMENTS AND RESULTS: Sleep recordings on 5,684 participants were deemed to be of sufficient quality to allow visual scoring of NREM and REM sleep. For each participant, we tabulated the frequency of transitions between wake, NREM sleep, and REM sleep. An overall transition rate was determined as the number of all transitions per hour sleep. Stage-specific transition rates between wake, NREM sleep, and REM sleep were also determined. A 5-point Likert scale was used to assess the subjective experience of restless and light sleep the morning after the sleep study. Multivariable regression models showed that a high overall sleep stage transition rate was associated with restless and light sleep independent of several covariates including total sleep time, percentages of sleep stages, wake time after sleep onset, and the arousal index. Compared to the lowest quartile of the overall transition rate (<7.76 events/h), the odds ratios for restless sleep were 1.27, 1.42, and 1.38, for the second (7.77-10.10 events/h), third (10.11-13.34 events/h), and fourth (>=13.35 events/h) quartiles, respectively. Analysis of stage-specific transition rates showed that transitions between wake and NREM sleep were also independently associated with restless and light sleep.

CONCLUSIONS: Assessing overall and stage-specific transition rates provides a complementary approach for assessing sleep continuity. Incorporating such measures, along with conventional metrics, could yield useful insights into the significance of sleep continuity for clinical outcomes.

}, keywords = {Age Factors, Aged, Arousal, Body Mass Index, Cohort Studies, Female, Humans, Male, Middle Aged, Polysomnography, Predictive Value of Tests, Risk Factors, Sex Factors, Sleep Stages, Sleep Wake Disorders}, issn = {0161-8105}, author = {Laffan, Alison and Caffo, Brian and Swihart, Bruce J and Punjabi, Naresh M} }